import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
from data import voc, coco
import os


class SSD(nn.Module):
    """Single Shot Multibox Architecture
    The network is composed of a base VGG network followed by the
    added multibox conv layers.  Each multibox layer branches into
        1) conv2d for class conf scores
        2) conv2d for localization predictions
        3) associated priorbox layer to produce default bounding
           boxes specific to the layer's feature map size.
    See: https://arxiv.org/pdf/1512.02325.pdf for more details.

    Args:
        phase: (string) Can be "test" or "train"
        size: input image size
        base: VGG16 layers for input, size of either 300 or 500
        extras: extra layers that feed to multibox loc and conf layers
        head: "multibox head" consists of loc and conf conv layers
    """
    def __init__(self, phase, size, base, extras, head, num_classes):
        super(SSD, self).__init__()
        self.phase = phase
        self.num_classes = num_classes
        self.cfg = (coco, voc)[num_classes == 21]
        self.priorbox = PriorBox(self.cfg)
        self.priors = Variable(self.priorbox.forward(), volatile=True)
        self.size = size

        #SSD network
        self.vgg = nn.ModuleList(base)
        #Layer learns to scale the l2 normalized features from conv4_3
        self.L2Norm = L2Norm(512, 20)
        self.extras = nn.ModuleList(extras)

        self.loc = nn.ModuleList(head[0])
        self.conf = nn.ModuleList(head[1])

        if phase == 'test':
            self.softmax = nn.Softmax(dim=-1)
            self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)

    def forward(self, x):
        """Applies network layers and ops on input image(s) x.

        Args:
            x: input image or batch of images. Shape: [batch,3,300,300].

        Return:
            Depending on phase:
            test:
                Variable(tensor) of output class label predictions,
                confidence score, and corresponding location predictions for
                each object detected. Shape: [batch,topk,7]

            train:
                list of concat outputs from:
                    1: confidence layers, Shape: [batch*num_priors,num_classes]
                    2: localization layers, Shape: [batch,num_priors*4]
                    3: priorbox layers, Shape: [2,num_priors*4]
        """
        sources = list()                    #保存需要分类和回归的特征图
        loc = list()
        conf = list()

        #apply vgg up to conv4_3 relu
        for k in range(23):
            x = self.vgg[k](x)

        #论文中第一次进行特征图分类和回归之前使用了Normalization
        s = self.L2Norm(x)
        sources.append(s)

        #apply vgg up to fc7
        for k in range(23, len(self.vgg)):
            x = self.vgg[k](x)
        sources.append(x)

        #apply extra layers and cache source layer outputs
        for k, v in enumerate(self.extras):
            x = F.relu(v(x), inplace=True)
            if k % 2 == 1:
                sources.append(x)

        #apply multibox head to source layers
        for (x, l, c) in zip(sources, self.loc, self.conf):
            loc.append(l(x).permute(0, 2, 3, 1).contiguous())
            conf.append(c(x).permute(0, 2, 3, 1).contiguous())
        if self.phase == 'test':
            output = self.detect(
                loc.view(loc.size(0), -1, 4),                   #loc preds
                self.softmax(conf.view(conf.size(0), -1, self.num_classes)),        #conf preds
                self.priors.type(type(x.data))                                      #defaut boxes
            )
        else:
            output = (
                loc.view(loc.size(0), -1, 4),
                conf.view(conf.size(0), -1, self.num_classes),
                self.priors
            )
        return output

    def load_weights(self, base_file):
        other, ext = os.path.splitext(base_file)
        if ext == '.pkl' or '.pth':
            print('Loading weigths into state dict...')
            self.load_state_dict(torch.load(base_file,
                                            map_location=lambda storage, loc:storage))
            print('Finished')
        else:
            print('Sorry only .pth and .pkl files supported.')



def vgg(cfg, i, batch_norm=False):
    layers = []
    in_channels = i
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        elif v == 'C':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]           #https://blog.csdn.net/GZHermit/article/details/79351803
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
    conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)          #https://blog.csdn.net/g11d111/article/details/82665265
    conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
    layers += [pool5, conv6, nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
    return layers

def add_extras(cfg, i, batch_norm=False):
    # Extra layers added to VGG for feature scaling
    layers = []
    in_channels = i
    flag = False
    for k, v in enumerate(cfg):
        if in_channels != 'S':
            if v == 'S':
                layers += [nn.Conv2d(in_channels, cfg[k + 1],
                            kernel_size=(1, 3)[flag], stride=2, padding=1)]
            else:
                layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
            flag = not flag
        in_channels = v
    return layers

def multibox(vgg, extra_layers, cfg, num_classes):
    loc_layers = []
    conf_layers = []
    vgg_source = [21, -2]
    for k, v in enumerate(vgg_source):
        loc_layers += [nn.Conv2d(vgg[v].out_channels,
                                 cfg[k] * 4, kernel_size=3, padding=1)]
        conf_layers += [nn.Conv2d(vgg[v].out_channels,
                                  cfg[k] * num_classes, kernel_size=3, padding=1)]
    #这里的enumerate()函数用于遍历序列中的元素以及它们的下标,从下标为2开始取，这里的[1::2]是从第二个索引开始取值，并且每次跳越2
    for k, v in enumerate(extra_layers[1::2], 2):
        loc_layers += [nn.Conv2d(v.out_channels, cfg[k]*4, kernel_size=3, padding=1)]
        conf_layers += [nn.Conv2d(v.out_channels, cfg[k]*num_classes, kernel_size=3, padding=1)]

    return vgg, extra_layers, (loc_layers, conf_layers)




base = {
    '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M', 512, 512, 512],
    '500': [],
}

extras = {
    '300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
    '500': [],
}

mbox = {
    '300': [4, 6, 6, 6, 4, 4],           #number of boxes per feature map location
    '512': [],
}

def build_ssd(phase, size=300, num_classes=21):
    if phase !="test" and phase !="train":
        print("ERROR:Phase: "+phase+"not recognized")
        return
    if size != 300:
        print("ERROR:You specified size "+repr(size)+". However, "+"currently only SSD300(size=300)is supported")
        return
    base_, extras_, head_ = multibox(vgg(base[str(size)], 3),
                                     add_extras(extras[str(size)], 1024),
                                     mbox[str(size)], num_classes)
    return SSD(phase, size, base_, extras_, head_, num_classes)
